{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "threatened-orbit",
   "metadata": {},
   "source": [
    "# Air Passengers Dataset\n",
    "\n",
    "In this notebook we will prepare and store the air passengers dataset found [here](https://github.com/facebook/prophet/blob/main/examples/example_air_passengers.csv).\n",
    "\n",
    "**Citation:**\n",
    "\n",
    "Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976) Time Series Analysis, Forecasting and Control. Third Edition. Holden-Day. Series G.\n",
    "\n",
    "**Description of data:**\n",
    "\n",
    "The data is a monthly time series measuring the number of international airline passengers, in thousands, from 1949 to 1960."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aggressive-license",
   "metadata": {},
   "source": [
    "In this notebook we will:\n",
    "\n",
    "1. Provide instructions to download the air passengers data set\n",
    "\n",
    "2. Save the time series data in the correct location for use in the course\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "competitive-robertson",
   "metadata": {},
   "source": [
    "# Get the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "outdoor-architecture",
   "metadata": {},
   "source": [
    "The dataset can be obtained from this [link](https://raw.githubusercontent.com/facebook/prophet/master/examples/example_air_passengers.csv). It will open a raw file in GitHub. A simple way of obtaining the data is to copy and paste the values from your browser into a text editor of your choice. \n",
    "Save it in the Datasets directory, which is found at the root of this project, with the filename `example_air_passengers.csv`.\n",
    "\n",
    "Alternatively, run the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5045cf1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/facebook/prophet/main/examples/example_air_passengers.csv\"\n",
    "df = pd.read_csv(url)\n",
    "df.to_csv(\"../Datasets/example_air_passengers.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "respected-worth",
   "metadata": {},
   "source": [
    "# Data set synopsis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mediterranean-toilet",
   "metadata": {},
   "source": [
    "The air passengers dataset is a monthly timeseries representing the number of US air passengers collected between January 1949 and December 1960."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "italic-serial",
   "metadata": {},
   "source": [
    "# Check that you can load the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "established-clinic",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "developmental-roulette",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\n",
    "    \"../Datasets/example_air_passengers.csv\", parse_dates=[\"ds\"], index_col=[\"ds\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "quantitative-missouri",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              y\n",
       "ds             \n",
       "1949-01-01  112\n",
       "1949-02-01  118\n",
       "1949-03-01  132\n",
       "1949-04-01  129\n",
       "1949-05-01  121"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "straight-mouth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ds'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaba673e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
